Based on the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification, we predict the level of empathic concern and personal distress displayed in essays. For the first stage of this project we implemented a Feed-Forward Neural Network using sentence-level embeddings as features. We experimented with four different embedding models for generating the inputs to the neural network. The subsequent stage builds upon the previous work and we have implemented three types of revisions. The first revision focuses on the enhancements to the model architecture and the training approach. The second revision focuses on handling class imbalance using stratified data sampling. The third revision focuses on leveraging lexical resources, where we apply four different resources to enrich the features associated with the dataset. During the final stage of this project, we have created the final end-to-end system for the primary task using an ensemble of models to revise primary task performance. Additionally, as part of the final stage, these approaches have been adapted to the WASSA 2023 Shared Task on Empathy Emotion and Personality Detection in Interactions, in which the empathic concern, emotion polarity, and emotion intensity in dyadic text conversations are predicted.

本研究针对WASSA 2022共情检测与情感分类共享任务，预测文章中表现出的共情关怀与个人困扰水平。该研究提出了一种新的模型架构及训练方法，结合分层数据采样和不同的词汇资源，显著提升了预测的准确性，并将其应用于2023年的对话情感和个性检测任务，展现出该方法在多轮对话分析中的潜在影响。

Towards More Accurate Prediction of Human Empathy and Emotion in Text
  and Multi-turn Conversations by Combining Advanced NLP, Transformers-based
  Networks, and Linguistic Methodologies

朝着更准确的预测文本和多轮对话中的人类共情与情感，结合先进的自然语言处理、基于变换器的网络与语言方法